Deep Convolution Neural Networks, or Deep CNN is the most core of the remarkable development in the field of Deep Learning. Though the CNN has been employed to solve character recognition problems in 1990s, it is not until recently that the CNN has become widespread in Machine Learning. For example, in 2012, the CNN significantly outperformed its competitors in an annual software contest, the ImageNet Large Scale Visual Recognition Challenge, and won the contest. After that, the CNN has become a very useful tool in the field of the machine learning.
Meanwhile, also in the field of autonomous driving, the CNN is used widely. In autonomous driving circumstances, most of input images have a similar typical arrangement, mainly including roads in the centers of the input images and sidewalks in both sides of the input images. Thus, CNN for the autonomous driving may learn parameters by using training images with the typical arrangement, e.g., composition.
However, learning process as shown above has a critical shortcoming. That is, in case arrangements of the input images are not similar to those of the training images, CNN operations may be inefficient. For example, when rounding a corner, a road may not exist in the center of a test image, different from the training images with the typical arrangement. Because parameters of the CNN are optimized for the input images with the roads located in centers thereof, the test image may not be processed properly with the above-mentioned parameters.